Mefail Tahiri1* Ejup Rustemi 

1University of Tetova, North Macedonia

2University of Tetova, North Macedonia




The machine intelligence of algorithms is now dispersed in a cloud computing environment, which will help businesses in the future find insightful information and carry out various tasks using APIs. Due to the fact that it meets economies of scale in a distributed setting, organizations are mass producing algorithms. With machine intelligence platforms that use machine learning to quickly prototype and deploy in production from sandboxes, artificial intelligence is the next fire for igniting the AI cold (which lasted from the 1990s through the 2010s). Recent times have seen the release of a number of open-source machine learning and deep learning platforms, including TensorFlow by Google, Caffe by the University of Berkeley, NLTK (Natural Language Tookit) by the University of Pennsylvania for natural language processing, Scikit-learn for Python, a number of R packages for deep learning and machine learning, Theano, a Python library for numerical computation, and Torch, a platform for developing machine learning algorithms. Big data has been greatly impacted by artificial intelligence, which has changed numerous global sectors by resolving previously intractable problems. This paper aims to evaluate the need for powerful processing infrastrucure for successful application of data science.

Keywords: data science, parallel computing, big data, machine learning, multiprocessing.

Volume 8. No.1(2023)

ISSN 2661-2666 (Online) International Scientific Journal Monte (ISJM)
ISSN 2661-264X (Print)



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This is an open-access article under the CC BY-NC-ND license (